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Delamination is a prevalent type of damage in composite laminate structures. Its accumulation degrades structural performance and threatens the safety and integrity of aircraft. This study presents a method for the quantitative identification of delamination identification in composite materials, leveraging distributed optical fiber sensors and a model updating approach. Initially, a numerical analysis is performed to establish a parameterized finite element model of the composite plate. Then, this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations. The radial basis function neural network surrogate model is then constructed based on the numerical simulation results and strain responses captured from the distributed fiber optic sensors. Finally, a multi-island genetic algorithm is employed for global optimization to identify the size and location of the damage. The efficacy of the proposed method is validated through numerical examples and experiment studies, examining the correlations between damage location, damage size, and strain responses. The findings confirm that the model updating technique, in conjunction with distributed fiber optic sensors, can precisely identify delamination in composite structures. [ABSTRACT FROM AUTHOR]

An accurate finite element model (FEM) plays a critical role in the structural damage identification. However, due to the existence of the uncertainties, such as material properties and modeling errors, it always exists some gaps between the analytical FEM and experimental structure. While an artificial neural network (ANN)-based model updating methods have been widely adopted to narrow the gap and obtain a baseline FEM, it still faces inaccurate results and fails to meet the physical law. In this regard, the study proposes a novel physics-based loss function inspired by modal sensitivity analysis and incorporates it into the residual neural network, thereby forming a novel physics-guided neural network (PGNN) method. The mapping relationship between the input of structural responses and output of model updating variables is constrained to retain its physical meaning by guiding the training process instead of pure data association, which aims to improve the accuracy of the ANN-based method and achieve accurate and high-efficiency model updating. An experimental example of a continuous rigid frame bridge is adopted to verify the feasibility of the proposed method. Additionally, other common model updating methods, including moth-flame optimization and regularization method, are used to make a comparison. The noise-robustness of the proposed method is investigated as well. Compared to the existing method, the results illustrate that the proposed PGNN method can achieve better model updating and good noise-robustness under high uncertainties, which means the introduction of the physics-based loss function significantly enhances the parameters updating ability of the neural network. The proposed method exhibits high efficiency and promising potential for large-scale bridge structure model updating. [ABSTRACT FROM AUTHOR]

Model reduction via spectral submanifolds (SSMs) has displayed benefits such as the facilitation of nonlinear analysis and significant speed-up gains. One needs the sensitivity of the SSM-based model reduction to carry over these benefits to the settings of optimal design, modal updating, and uncertainty quantification of high-dimensional nonlinear mechanical systems. Here, we construct explicit third-order, SSM-based model reduction for general mechanical systems. We further derive the explicit sensitivity of the third-order SSM-based reduction using direct and adjoint methods. We demonstrate the effectiveness of the derived explicit sensitivity via a few examples with increasing complexity. We also show that the obtained sensitivity can be used to effectively construct perturbed SSMs, backbone curves, and forced response curves. [ABSTRACT FROM AUTHOR]

This paper presents a numerical and experimental study aimed at the modeling and dynamic characterization of the reinforced concrete structure of the Palazzetto dello Sport in Rome, designed and by Pier Luigi Nervi with Annibale Vitellozzi, and built by Nervi & Bartoli contractors in 1956‐57. An experimental dynamic testing campaign has been performed to obtain the modal properties of the structure, identified using operational modal analysis (OMA). The axial symmetry of the Palazzetto's dome, expected to exist in an idealized perfect dome, has been observed to occur experimentally by noting that rotated modes possessed nearly identical frequencies, evidenced by closely spaced double peaks in the power spectral density. This observation recognizes the remarkable precision of Nervi's construction methodology. A numerical 3D model has been developed by relying on detailed information about the structure gathered from various sources, including archival documents, on‐site testing, and surveying. The model has been calibrated by means of modal updating based on the experimental measurements collected in this study. The reconstruction of the dome using laser‐scanning and aerophotogrammetry has revealed a slight asymmetry in its thickness distribution (and consequently its mass distribution) that, when incorporated in the numerical model, has been shown to contribute to the experimentally observed frequency split. It is expected that, by tracking these closely spaced frequencies on top of the typical variations of natural frequencies in a health monitoring approach, further insight might be gained into the detection of possible damages and/or degradation of the structure and its components. [ABSTRACT FROM AUTHOR]

Spectral multivariate calibration aims to derive models characterizing mathematical relationships between sample analyte amounts and corresponding spectral responses. These models are effective at predicting target domain sample analyte amounts when target samples are within the analyte and spectral calibration source domain. Models fail when target samples shift (analyte amounts and/or spectra) from the original calibration domain model. A total recalibration solution requires acquisition of new sample reference values and spectra. However, obtaining enough reference values to distinguish the target domain may be challenging or expensive. A simpler approach adapts the original model to the target domain using target sample spectra without analyte reference values (unlabeled). Analytical chemists have developed several machine learning algorithms using unlabeled regression domain adaptation processes. Unfortunately, prediction accuracy declines for these methods depending on how much the target domain analyte distribution has shifted from the calibration distribution, and regression transfer learning methods are instead needed. Regression domain adaptation and transfer learning are often referred to as model updating in analytical chemistry, but regression domain adaptation only applies to spectral shifts. The regression transfer learning method presented in this paper named null augmentation regression constant analyte (NARCA) leverages unlabeled repeat spectra of a single target sample to update an original calibration model to the shifted target domain sample. With sample repeat spectra, the analyte amount can be assumed constant or nearly constant for NARCA and because models are formed for one sample, NARCA operates as a local modeling method. The performance of NARCA as a regression transfer learning method is evaluated using five near-infrared data sets. [ABSTRACT FROM AUTHOR]

This paper describes the experiments carried out on a mediaeval masonry tower in the historic centre of Lucca and some finite element numerical simulations of the tower's experimental response. The Guinigi Tower, one of the most iconic monuments in Lucca, has been continuously monitored by high-sensitivity seismic stations that recorded the structure's response to the dynamic actions of the surrounding environment. The monitoring campaign results have been analysed to show the effectiveness of dynamic monitoring as a valuable source of information on the structural properties of the tower. The dynamic analyses of the tower and the surrounding palace subjected to some seismic events recorded during the experiments have highlighted the capabilities of experiment-based finite element modelling. The calibration of the finite element model and the numerical analysis have been carried out by resorting to procedures developed at ISTI-CNR and able to consider the nonlinear behaviour of masonry materials. [ABSTRACT FROM AUTHOR]

The significance of model updating methods is becoming increasingly evident as the demand for greater precision in numerical models rises. In recent years, with the advancement of deep learning technology, model updating methods based on various deep learning algorithms have begun to emerge. These methods tend to be complicated in terms of methodological architectures and mathematical processes. This paper introduces an innovative model updating approach using a deep learning model: the deep neural network (DNN). This approach diverges from conventional methods by streamlining the process, directly utilizing the results of modal analysis and numerical model simulations as deep learning input, bypassing any additional complex mathematical calculations. Moreover, with a minimalist neural network architecture, a model updating method has been developed that achieves both accuracy and efficiency. This distinctive application of DNN has seldom been applied previously to model updating. Furthermore, this research investigates the impact of prefabricated partition walls on the overall stiffness of buildings, a field that has received limited attention in the previous studies. The main finding was that the deep neural network method achieved a Modal Assurance Criterion (MAC) value exceeding 0.99 for model updating in the minimally disturbed 1st and 2nd order modes when compared to actual measurements. Additionally, it was discovered that prefabricated partitions exhibited a stiffness ratio of about 0.2–0.3 compared to shear walls of the same material and thickness, emphasizing their role in structural behavior. [ABSTRACT FROM AUTHOR]

To obtain a precise finite-element model (FEM) for analyzing the dynamic response of corroded beams at high temperatures, a stepwise FEM modification strategy is proposed based on the improved extreme learning machine. Three concrete beams were designed and cast, and the dynamic response characteristics of corroded concrete beams at room temperature and high temperature are discussed. Firstly, electrical accelerated corrosion tests and vibration tests were conducted on simply supported beams at room temperature. The fundamental frequencies of concrete beams under different corrosion ratios were measured. The attenuation law of fundamental frequency with corrosion ratio also was studied. Subsequently, the FEM under different corrosion ratios was modified. The bond-slip between steel bars and concrete under different degrees of corrosion was considered during the correction process. Finally, a vibration test at high temperature was performed. The modal attenuation law of corroded beams at high temperatures was analyzed. Based on the modified FEM, numerical analysis at high temperature was performed. The proposed FEM modification strategy and the study of the attenuation regularities of modal information under fire exposure provide a foundation for further research on the damage development of corroded reinforced concrete (RC) beams under fire exposure. [ABSTRACT FROM AUTHOR]

Abstract In addressing the finite element model and actual structural error of the sprayer boom truss, this study aims to achieve high-precision dynamic characteristics, enhance simulation credibility, make informed optimization decisions, and reduce testing costs. The research investigates the dynamic behavior of the sprayer boom truss through modal experiments and finite element simulations. Initially, modal parameters of the sprayer boom are obtained through experimental testing, validating their reasonableness and reliability. Subsequently, Ansys Workbench18.0 simulation software was employed to analyze the finite element model of the sprayer boom, revealing a maximum relative error of 11.93% compared to experimental results. To improve accuracy, a kriging-based response surface model was constructed, and multi-objective parameter adjustments using the MOGA algorithm reduce the maximum relative error to 4.6%. Sensitivity analysis further refines the model by optimizing target parameters, resulting in a maximum relative error of 4.96%. These findings demonstrate the effective enhancement of the corrected finite element model’s precision, with the response surface method outperforming sensitivity analysis the maximum relative error between the updated finite element model and experimental results was within the engineering allowable range, confirming the effectiveness of the updated model.

Summary: Acceleration measurements are often used for model updating of civil engineering structures, especially in the case of seismic monitoring. It is yet unclear if accelerations alone would generate an accurate and robust finite‐element (FE) model. This study examines this notion and analyzes the possibility of using other vibration monitoring data for model updating of shear‐wall tall buildings. This study compares the accuracy and robustness of the FE models being optimized via accelerations, roof displacement, wall rotations, interstory drift ratios, and the linear combination of these measurements. A numerical case study is analyzed using Timoshenko beams for modeling the lateral vibration of a benchmark 42‐story building under seismic excitations. Results show that the acceleration response of the examined building is mostly governed by its higher vibration modes. Depending on the characteristics of ground motions, using accelerations alone may generate an FE model biased towards higher‐order modes without effectively capturing the lower‐order modes. For instance, the first modal frequency of the updated FE model could be 12.0% lower than the true value, and the reconstructed displacement and rotation responses are noticeably inaccurate. Employing multi‐source monitoring data for model updating, for example, the combinations of roof displacement and acceleration measurements, could reduce the normalized root‐mean‐square errors in displacements by more than 70%. This study also quantifies the robustness of the FE model under various measurement noise levels and 50 pairs of earthquake records. Finally, the effects of multi‐source data on FE model updating are validated via experiments on a 7‐story shear wall building. Analysis reveals that a more accurate and robust FE model can be determined via a combination of accelerations and top displacement than via acceleration alone. [ABSTRACT FROM AUTHOR]

In this study, we study the tracking of the molten metal region in the dross removal process during metal ingot casting, and propose a real-time tracking method based on adaptive feature selection and weighted histogram. This research is highly significant in metal smelting, as efficient molten metal tracking is crucial for effective dross removal and ensuring the quality of metal ingots. Due to the influence of illumination and temperature in the tracking environment, it is difficult to extract suitable features for tracking molten metal during the metal pouring process using industrial cameras. We transform the images captured by the camera into a multi-scale feature space and select the features with the maximum distinction between the molten metal region and its surrounding background for tracking. Furthermore, we introduce a weighted histogram based on the pixel values of the target region into the mean-shift tracking algorithm to improve tracking accuracy. During the tracking process, the target model updates based on changes in the molten metal region across frames. Experimental tests confirm that this tracking method meets practical requirements, effectively addressing key challenges in molten metal tracking and providing reliable support for the dross removal process. [ABSTRACT FROM AUTHOR]

The elastic modulus of concrete is one of the most important parameters in the analysis and design of concrete structures. However, determining the elastic modulus in civil structures using core-drilled samples is time-consuming and labor-intensive. Additionally, the elastic modulus of Ultra-High Performance Concrete (UHPC) varies significantly depending on its composition. This paper proposes an improved, non-destructive application to identify the elastic modulus of UHPC materials in in-service structures. The elastic modulus is estimated through calibration between a numerical model and experimental UHPC plate vibration test results, using frequency and mode shapes. This calibration involves solving an inverse problem using optimization techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search, and the YUKI algorithm. Updating the plate characteristics is made possible by the development of numerous iterations, where each iteration updates the elastic modulus, thickness, and width values in the term to find the best solution. The highest accuracies compared to experimental data natural frequency values were found in models updated by GA, PSO, YUKI, and Cuckoo algorithms, with errors of 10.77%, 6.58%, 6.87%, and 6.87%, respectively. An experimental sample was tested to determine the elastic modulus of the UHPC, and the proposed application showed a 0.55% error compared to the experimental value. Thus, the estimated elastic modulus value is highly accurate. [ABSTRACT FROM AUTHOR]

Aiming to enhance the accuracy, stability, and noise robustness of swarm intelligence-based algorithms for structural damage identification (SDI), a novel improved dragonfly algorithm (IDA) is proposed. The IDA integrates the dragonfly algorithm (DA) with three key strategies including enhanced Lévy flight, optimal solution bidirectional search, and greedy preservation. These strategies are introduced to enhance the exploration capability of the original DA and improve the IDA's capacity to obtain global optima. An objective function is defined using frequency change ratio and flexibility assurance criterion (FAC). Additionally, trace sparse regularization is also incorporated into the objective function since most of the damages in structures tend to be sparsely distributed, so that a sparse result is ensured to improve SDI accuracy. To evaluate the performance of the proposed algorithm, a comparison of the original DA and IDA is conducted using four benchmark functions. The results demonstrate that the proposed algorithm achieves improved convergent rate and accuracy. Furthermore, numerical simulations are performed on a 10-element simply-supported beam and a 31-element planar truss to validate the effectiveness and efficiency of the proposed algorithm in SDI. Significantly, the utilization of IDA instead of DA leads to a substantial reduction in the average calculated relative error for the truly damaged element within the considered damage cases of the simply-supported beam, decreasing from 13.05% to 6.15%. Moreover, an experimental simply-supported beam structure with several assumed damage cases is fabricated in the laboratory. The experimental results further confirm the robustness and capability of the proposed method in real-world SDI applications. [ABSTRACT FROM AUTHOR]

Combining the accurate physical description of high‐fidelity mechanical formulations with the practical versatility of low‐order discrete models is a fundamental and open‐ended topic in structural dynamics. Finding a well‐balanced compromise between the opposite requirements of representativeness and synthesis is a delicate and challenging task. The paper systematizes a consistent methodological strategy to identify a physics‐based reduced‐order model (ROM) preserving the physical accuracy of large‐sized models with distributed parameters (REM), without resorting to classical techniques of dimensionality reduction. The leading idea is, first, to select a limited configurational set of representative degrees of freedom contributing significantly to the dynamic response (model reduction) and, second, to address an inverse indeterminate eigenproblem to identify the matrices governing the linear equations of undamped motion (structural identification). The physical representativeness of the identified model is guaranteed by imposing the exact coincidence of a selectable subset of natural frequencies and modes (partial isospectrality). The inverse eigenproblem is solved analytically and parametrically, since its indeterminacy can be circumvented by selecting the lumped mass matrix as the primary unknown and the stiffness matrix as a parameter (or vice versa). Therefore, explicit formulas are provided for the mass matrix of the ROM having the desired low dimension and possessing the selected partial isospectrality with the REM. Minor adjustments are also outlined to remove a posteriori unphysical effects, such as defects in the matrix symmetry, which are intrinsic consequences of the algebraic identification procedure. The direct and inverse eigenproblem solutions are explored through parametric analyses concerning a multistory frame, by adopting a high‐fidelity Finite Element model as REM and an Equivalent Frame model as ROM. Before mass matrix identification, modal analysis results indicate a general tendency of ROM to underestimate natural frequencies, with the underestimation strongly depending on the actual mass distribution of the structure. After the identification of the mass matrix and the elimination of unphysical defects, isospectrality is successfully achieved. Finally, extensions to prototypical highly massive masonry buildings are presented. The qualitative and quantitative discussion of the results under variation of the significant mechanical parameters provides useful insights to recognize the validity limits of the approximations affecting low‐order models with lumped parameters. [ABSTRACT FROM AUTHOR]

There are deviations between the radio telescope antenna finite element (FE) model, founded on the design stage, and the actual working antenna structure. The original FE model cannot accurately describe the antenna structure deformation characteristics under the environmental load, thereby compromising the accuracy of the active structural compensation. This article proposes an antenna FE model updating method founded on parameter optimization with a surrogate model. The updating method updates the modulus of elasticity parameters of different components of the antenna backup structure (BUS) to obtain finite element analysis (FEA) results consistent with the actual measurement of the antenna reflector surface shape. The surrogate model founded on the multi-quadratic radial basis function (RBF) improves the computational efficiency of FE model updating, replacing the complex and time-consuming finite element analysis and calculation process. This method is implemented on a radio telescope antenna with an aperture of 25 m. The results show a significant reduction in the mismatch between the antenna and the updated FE model. This method's calculation time is significantly reduced compared with the updating method without using the surrogate model, with the RBF surrogate model taking 1% of the time of the finite element model in the FEA calculations. The proposed method can improve the antenna FE model calculation accuracy and significantly enhance the efficiency of FE model updating calculations. Thus, it can offer a reference for antenna engineering practice. [ABSTRACT FROM AUTHOR]

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A lot of bridges are aging and reaching the anticipated service life. To gain insight in their remaining resistance, inspections and measurements can be performed. The resulting information and data can be used to update variables in the degradation models of these bridges. To account for spatial variation, degradation variables can be modeled with random fields. The random fields of the degradation variables are updated based on a Bayesian inference procedure, where different types of heterogenous and indirect data are accounted for. Nevertheless, in this procedure, various assumptions need to be made, such as the quantification of measurement uncertainty, the structural and degradation model to be used, the prior distributions of the variables of interest, and so forth. These assumptions can influence the results of the Bayesian inference. Hence, in this work, it will be investigated what the influence is of different assumptions and how they affect the localization and quantification of damage. This will be done by application to a reinforced concrete girder bridge. The main conclusions are that the model used in the Bayesian inference procedure should resemble the actual structure as good as possible and that simplifications that are allowed in the design can lead to posterior distributions deviating a lot from the actual situation. Moreover, it is illustrated how information from visual observations can be included in the definition of the prior distributions and how this has a beneficial effect on the posterior predictions. [ABSTRACT FROM AUTHOR]

Accurate estimation of rotational frequency response functions (FRFs) is an essential element of successful structural coupling. It is well known that the experimental estimation of structural excitations is very difficult with current technology. This paper proposes a scheme to improve the performance of the frequency-based substructuring (FBS) method by estimating unmeasured FRFs, including those corresponding to rotational degrees of freedom, from a set of experimentally determined translational FRFs. More specifically, the modal parameters extracted by modal analysis (EMA) from the experimentally determined FRFs are used for model updating, modal expansion and FRF synthesis. For this purpose, an approximate modelling approach is proposed, where a simplified and approximate finite element model (ASFE) is developed and updated to accurately reproduce the experimental responses. A modal expansion basis is then constructed from the ASFE to expand the mode shapes using the system equivalent reduction and expansion process (SEREP). FRF synthesis is then used to derive unmeasured translational and rotational FRFs. The synthesised FRFs within the frequency range of interest agree well with the experimental FRFs. The synthesised full FRF matrix is then used with the FBS method to derive the response model for the coupled structure in a bottom-up modelling approach. [ABSTRACT FROM AUTHOR]

During bridge service, material degradation and aging occur, affecting bridge functionality. Bridge health monitoring, crucial for detecting structural damage, includes finite element model modification as a key aspect. Current finite element-based model updating techniques are computationally intensive and lack practicality. Additionally, changes in loading and material property deterioration lead to parameter uncertainty in engineering structures. To enhance computational efficiency and accommodate parameter uncertainty, this study proposes a Gaussian process model-based approach for predicting structural natural frequencies and correcting finite element models. Taking a simply supported beam structure as an example, the elastic modulus and mass density of the structure are sampled by the Sobol sequence. Then, we map the collected samples to the corresponding physical space, substitute them into the finite element model, and calculate the first three natural frequencies of the model. A Gaussian surrogate model was established for the natural frequency of the structure. By analyzing the first three natural frequencies of the simply supported beam, the elastic modulus and mass density of the structure are corrected. The error between the corrected values of elastic modulus and mass density and the calculated values of the finite element model is very small. This study demonstrates that Gaussian process models can improve calculation efficiency, fulfilling the dual objectives of predicting structural natural frequencies and adjusting model parameters. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) is of utmost importance for the preservation and safe operation of historical arch bridges. This paper presents the development of a SHM strategy aimed at the model-based damage assessment of masonry bridges using frequency data. Structural damage induces natural frequency changes that are strictly related to the damage location. Consequently, a numerical model capable of reproducing the intact dynamic characteristics should allow to simulate damage scenarios, including the observed one, with the anomaly localisation being performed through the similarity between the experimentally detected frequency changes and the numerically simulated ones. The proposed methodology is based on the availability of an appropriate knowledge of the investigated structure, allowing to define a Finite Element (FE) model that accurately reproduces the system dynamic characteristics. Hence, the SHM strategy involves: (a) the use of the calibrated model to simulate different damage scenarios, so that a Damage Location Reference Matrix (DLRM) is defined through the associated frequency shifts; (b) the damage detection through statistical pattern recognition of vibration data; (c) the damage localisation through the comparison between the identified frequency changes and those defined in the DLRM matrix. Pseudo-experimental monitoring data, referring to a historical masonry viaduct, were generated and used to exemplify the reliability and accuracy of the developed algorithms in detecting and localizing damage.

Structural health monitoring (SHM) system provides valuable information for the fatigue assessment of existing rail-cum-road bridges. This paper aims to develop an effective fatigue assessment approach for the rail-cum-road bridge, Jiujiang Yangtze River Bridge, by utilising the SHM data, focusing on dynamic modal analysis, finite element model updating and fatigue assessment. First, the traffic load spectrum and modal characteristics of the bridge are investigated from the SHM data. A three-dimensional finite element model is constructed and then updated by using the measured modal data through the proposed regularised model updating method. Then, the updated numerical model is verified with the measured dynamic response data, which can be utilised for calculating stress response at critical structural components of the rail-cum-road bridge. Finally, an improved Corten-Dolan’s model is proposed to analyse the fatigue damage and structural reliability of the critical structural components of the bridge, taking into account the combined effects of train and highway vehicle loads. The results demonstrate that the proposed fatigue assessment method provides more reliable results for the rail-cum-road bridge by considering the combined effect of multi-level traffic loads and the non-linear fatigue damage accumulation. It is concluded that the short H-shaped suspender is identified as the most vulnerable structural member of the rail-cum-road bridge, and the remaining fatigue service life of the typical components of the bridge should meet the design requirement.

Finite element models (FEMs) are effective for predicting structural behaviors subjected to various excitations. However, modeling errors in FEMs always exist, especially for complex structural systems, and these affect the accuracy of FEMs. Model updating using measured data can significantly improve model accuracy as it can closely match the predicted and measured responses. In this work, field vibration tests were carried out on a 250-meter super-tall building, and model updating was conducted using an enhanced Markov chain Monte Carlo (MCMC), developed based on Bayesian theory. The sampling process was divided into multiple levels, with each level having a sampling level, generating the target PDF, which is regarded as the bridge PDF. Kernel density estimation is used to adaptively construct the proposal PDF in each level so that the generated samples can move to the region of high probability smoothly level by level. Sensitivity analysis was carried out to investigate the efficiency of the proposed model updating algorithm. Different algorithmic parameters, including the number of uncertain parameters, initial samples of each parameter, the ratio of error variances between two levels, and the number of sampling levels, are discussed to study the performance of the algorithm on the application of the super-tall building.

The l 2 regularization is usually used to deal with the problems of under-determinacy and measurement noise for the conventional sensitivity-based model updating damage detection methods. However, the l 2 regularization technique often provides overly smooth solutions and thus cannot exhibit the sparsity of the structural damage due to the promotion of the 2-norm term on smoothness. In the study, a structural damage detection method is proposed based on an improved modal flexibility sensitivity function and an iterative reweighted l p (IR l p) regularization. Specifically, the sensitivity function is established by introducing changes in the mode shapes into the derivative of eigenvalue and can be applied to identify the localized damage more accurately. Additionally, IR l p regularization is proposed to deal with the ill-posed problem of damage detection in a noisy environment. The proposed IR l p regularization is compared with the l 1 and l 2 regularizations through a numerical and an experimental examples. The numerical and experimental results indicate that the IR l p regularization can more accurately locate and quantify the single and multiple damages under the noise situation. The maximum identification errors are only 5.16% and 5.67%, respectively. Moreover, compared to the basic modal flexibility sensitivity function, the improved function is more sensitive to the damage. The maximum identification error of the improved function is less than 6%, while the relative errors are significantly larger in the basic function. [ABSTRACT FROM AUTHOR]

Rail transit’s wheel–rail system periodically encounters defects such as wheel polygons, rail corrugation, and rail fastener failure, which are intricately linked to the modal parameters of track structures. Identifying these modal parameters is essential for refining wheel–rail dynamics models, understanding track defect mechanisms, and defect detection. This study reviews the current methodologies for identifying track structure modal parameters, emphasizing their significance in track engineering. It categorizes various identification techniques, examines their development, and highlights their application in updating track dynamics theoretical models. The relationship between track modal parameters and wheel–rail defects is discussed, alongside a summary of modal parameter-based defect remediation strategies globally. The paper also evaluates the current state of defect identification research utilizing track modal parameters. In the “prospects” section, three forward-looking research avenues are proposed. These approaches are poised to streamline and improve the efficiency of modal parameter extraction, marking potential breakthroughs in the field. [ABSTRACT FROM AUTHOR]

Model updating using multiple test data is usually a challenging task for frictional structures. The difficulty arises from the limitations of nonlinear models which often overlook the uncertainties inherent in contact interfaces and in actual test conditions. In this paper, we present a parametric study for the model calibration process of a friction-damped turbine blade, addressing the experimentally measured response variability in computational simulations. On the experimental side, a recently developed test setup imitating a turbomachinery application with mid-span dampers is used. This setup allows measuring multiple responses and contact forces under nominally identical macroscale conditions. On the computational side, the same system is modeled in a commercial finite element software, and nonlinear vibration analyses are performed with a specifically developed in-house code. In numerical simulations, the multivalued nature of Coulomb's law, which stems from the inherent variability range of static friction forces in permanently sticking contacts, is considered to be the main uncertainty. As the system undergoes vibration, this uncertainty propagates into the dynamic behavior, particularly under conditions of partial slip in contacts, thus resulting in response variability. A deterministic approach based on an optimization algorithm is pursued to predict the limits of the variability range. The model is iteratively calibrated to investigate the sensitivity of response limits to contact parameters and assembly misalignment. Through several iterations, we demonstrate how uncertain initial contact conditions can be numerically incorporated into dynamic analyses of friction-damped turbine blades. The results show a satisfactory level of accuracy between experiments and computational simulations. This work offers valuable insights for understanding what influences test rig response and provides practical solutions for numerical simulations to improve agreement with experimental results. [ABSTRACT FROM AUTHOR]

In this paper the actual dynamic behavior of the civic Clock tower of Rotella, a little village in central Italy heavily damaged by the recent 2016 seismic sequence, is thoroughly investigated by means of a detailed numerical model built and calibrated using the experimental modal properties obtained through Ambient Vibration Tests. The goal is to update the uncertain parameters of two behavioral material models applied to the Finite Element Model (elastic moduli, mass densities, constraints, and boundary conditions) to minimize the discrepancy between experimental and numerical dynamic features. A sensitivity analysis was performed with the definition of a metamodel to reduce the computational strain and try to define the necessary parameters to use for the calibration process. Due to the high nonlinear dependency of the objective function of this optimization problem on the parameters, and the likely possibility to get trapped in local minima, a machine learning approach was meant. A fully automated Finite Element Model updating procedure based on genetic algorithms and global optimization is used, leading to tower uncertain parameters identification. The results allowed to create a reference numerical replica of the structure in its actual health state and to assess its dynamic performances allowing better control over their future evolution. [ABSTRACT FROM AUTHOR]

Model updating procedures based on experimental data are commonly used in case of historic buildings to identify numerical models that are subsequently employed to assess their structural behaviour. The reliability of these models is closely related to their ability to account for all the uncertainties that are involved in the knowledge process. In this regard, to handle these uncertainties and quantify their propagation, Bayesian inference is frequently employed being able to deal with the effects of parameter uncertainty, observation errors and model inadequacy. The computation of the posterior distribution through Bayesian inference needs–however–the evaluation of the likelihood function, which requires solving complex multi-dimensional integration problems. To bridge this shortcoming, the paper compares two Bayesian inference approaches to show how different approximations affect the results of simulated inference: a discrete approach for the likelihood computation in the Bayesian Model Updating (BMU) and a Monte Carlo likelihood-free method known as Approximate Bayesian Computation (ABC) are reported. As reference, the typology of historic masonry towers was considered by using their natural frequencies as experimental data for model updating. The two procedures provide very similar results supporting the validity of both methods despite ABC turns out to be a more flexible approach. [ABSTRACT FROM AUTHOR]